The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applications such as hiring, however, decisions are made in multiple stage with additional information at each stage. In such cases, fairness issues…
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48 results for “local fairness”
This work addresses local fairness in machine learning models.
problem Ensuring fairness within subregions of feature space, not just global averages.
method Introduces ROAD, a Distributionally Robust Optimization (DRO) approach with adversarial learning.
result Achieves Pareto dominance in local fairness and accuracy across datasets.
WassFFed addresses fairness in Federated Learning by ensuring consistency between local and global models.
problem Achieving fairness in Federated Learning where data is distributed among diverse user groups.
method WassFFed employs a Wasserstein barycenter calculation to aggregate local models' outputs, ensuring consistency and fairness.
result WassFFed outperforms existing approaches in balancing accuracy and fairness.
Study fairness in ordinal regression using threshold models.
problem Fairness in ordinal regression predictions.
method Adapted fairness notions from fair ranking; use threshold model with scoring function and thresholds; apply binary classification for scoring function and local search for thresholds.
result Generalization guarantees on predictor error and fairness violation; effectiveness demonstrated in experiments.
The paper proposes a fair and private decentralized deep learning framework.
problem Ensuring fairness and privacy in collaborative deep learning.
method A reputation system and differential privacy are used. FDPDDL framework is built with two stages: initialisation and update.
result FDPDDL achieves high fairness, comparable accuracy to centralised and distributed frameworks, and better accuracy than standalone.
Optimal LDP mechanisms reduce data unfairness in classification.
problem Reducing data unfairness in classification models.
method Developed a closed-form optimal mechanism for binary attributes and a tractable framework for multi-valued attributes.
result Optimal LDP mechanisms improve fairness in classification while maintaining accuracy close to non-private models.
Model shows partial compliance can lead to less fair outcomes than expected.
problem How partial compliance affects fairness in competitive markets.
method Simple model of employment market, simulation to explore effects.
result Partial compliance can lead to less fair outcomes than expected.
Algorithm provides fair clustering guarantees for k-means and k-median.
problem Ensuring fair clustering guarantees for every point in a dataset.
method Local search algorithm for -median and -means, with individual fairness as a key metric.
result Achieves fair clustering with a constant factor approximation of the optimal solution.
UDJ-FL framework achieves multiple distributive justice-based fairness metrics in federated learning.
problem Ensuring fairness in federated learning across different client data distributions.
method UDJ-FL framework uses aleatoric uncertainty-based client weighing and fair resource allocation techniques.
result UDJ-FL achieves egalitarian, utilitarian, Rawls' difference principle, and desert-based fairness metrics.
New rule-based method for classification with scalability, interpretability, and fairness.
problem Developing a scalable and fair classification method.
method Column generation for linear programming, decision tree-based heuristic, and rule-based optimization.
result The method returns interpretable rules with optimal weights and addresses fairness constraints.
L-ARC improves model fairness by localizing risk guarantees.
problem Improving model fairness in tasks like image segmentation and wireless networks.
method Localized Adaptive Risk Control (L-ARC) updates a threshold function in RKHS to target localized statistical risk guarantees.
result L-ARC produces prediction sets with improved fairness across different data subpopulations.
CFFL framework improves fairness in FL without sacrificing accuracy.
problem Overfitting and lack of collaborative fairness in Federated Learning.
method CFFL framework uses reputation to ensure participants converge to different models.
result CFFL achieves high fairness, comparable accuracy, and better performance than Standalone and Distributed frameworks.
Proposes a method to identify causal relationships using background knowledge.
problem Identifying causal relationships in the presence of background knowledge.
method Learning local structure using all types of causal background knowledge (direct, non-ancestral, ancestral). Criteria for identifying causal relationships based on local structure.
result Effective and efficient method for local structure learning and causal relationship identification.
Improved analysis for fair federated learning reduces dependence on noise floor.
problem Asymptotic stationarity in group fair federated learning with reduced noise floor dependence.
method DS FedProxGrad framework with inexact local proximal solutions and fairness regularization.
result Algorithm converges asymptotically to stationarity without dependence on a noise floor.
Paper optimizes PCA for fairness using MMD and Stiefel manifold optimization.
problem Fair principal component analysis (PCA) to minimize MMD between protected classes.
method Formulates fair PCA as non-convex optimization over Stiefel manifold, solves using REPMS with theoretical guarantees.
result Our approach outperforms prior work in fairness, explained variance, and runtime.
Personalized federated learning improves model accuracy and fairness by leveraging shared representations and local memorization.
problem Sub-optimal performance of federated learning when client data distributions are heterogeneous.
method Proposes a personalization mechanism based on local memorization of shared representations from a collectively trained global model.
result Significantly higher accuracy and fairness compared to state-of-the-art methods.
The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggrega…
Post-processing corrects bias in ML systems without retraining.
problem Correcting bias in ML systems that are already in use.
method Proposes general post-processing algorithms for individual fairness based on graph Laplacian regularization.
result Empirically, post-processing algorithms correct individual biases in large-scale NLP models while preserving accuracy.
FairACE improves fairness in GNNs by balancing node performance across degree groups.
problem Degree biases in GNNs lead to unequal prediction performance among nodes with varying degrees.
method Integrates asymmetric contrastive learning with adversarial training to balance performance between high-degree and low-degree nodes.
result Significantly improves degree fairness metrics while maintaining competitive accuracy.
WAFFLe anonymizes federated learning weights to protect data privacy and fairness.
problem Federated learning exposes local models to attacks and underfits heterogeneous clients.
method Combines Indian Buffet Process with shared weight factors.
result Significant improvement in local test performance and fairness.
This paper improves privacy and fairness in federated learning by protecting sensitive data and ensuring group fairness.
problem Privacy and fairness issues in federated learning.
method Introduces group privacy through -privacy, a localized form of differential privacy.
result The method provides better group fairness than a global model in federated learning.
Scaff-PD improves fairness and robustness in federated learning with reduced communication.
problem Improving fairness and robustness in federated learning with limited communication.
method Scaff-PD uses a family of distributionally robust objectives and an accelerated primal dual algorithm with bias-corrected steps.
result Scaff-PD achieves significant gains in communication efficiency and convergence speed while maintaining fairness and robustness.
Cofrontalsmath.DG
In this paper we introduce the notion of cofrontal mappings, as the dual objects to frontal mappings, and study their basic local and global properties. Cofrontals are very special mappings and far from generic nor stable except for the case of submersions. It is observed that any smooth mapping can be -approximat…
FPCA optimizes fairness in target vectors' span.
problem Fairness in principal component analysis for multiple target vectors.
method Non-concave maximization of worst projected target norm using sub-gradient descent.
result Optimization landscape is benign with globally optimal local minima.
New framework enforces demographic parity on distribution tails.
problem Enforcing demographic parity on entire distribution can degrade accuracy.
method Optimal transport theory, focusing on distribution tails.
result More nuanced and context-sensitive fairness interventions.
This work analyzes fairness-accuracy trade-offs using causal methods.
problem Discriminatory behavior in machine learning systems based on sensitive characteristics.
method Introduces path-specific excess loss (PSEL) and causal fairness/utility ratio to quantify trade-offs.
result Shows how enforcing fairness constraints can reduce discrimination while increasing loss.
Local discovery method uncovers direct unfairness in complex systems.
problem Identifying causal pathways of unfairness in complex domains.
method Local discovery for direct discrimination (LD3) method.
result LD3 returns a valid adjustment set (VAS) for assessing unfairness.
Handling model risk with XVAsq-fin.PR
Paper revisits HVA to address model risk in banking.
problem Model risk and dynamic hedging frictions in banking.
method Reconciles global fair valuation with local bank models.
result Local models should be excluded rather than managed via reserves.
Paper refutes EM convergence theory and introduces a new EM algorithm.
problem The convergence theory of the EM algorithm is incorrect and affects its performance.
method Proposes a new EM algorithm called the Channel Matching (CM) EM algorithm and provides an initialization map.
result The locally maximal Q can affect the convergent speed but not the global convergence.
Fair Wasserstein Coresetsstat.ML
FWC creates fair synthetic samples for machine learning tasks.
problem Addressing biases in machine learning models for fair decision-making.
method FWC uses an efficient majority minimization algorithm to minimize Wasserstein distance while enforcing demographic parity.
result FWC achieves a competitive fairness-utility tradeoff and reduces biases in predictions from large language models.
In the paper, we introduce the notion of a local regular supermartingale relative to a convex set of equivalent measures and prove for it the necessary and sufficient conditions of optional Doob decomposition in the discrete case. This Theorem is a generalization of the famous Doob decomposition onto the case of superm…
Unified framework for combinatorial and rounding algorithms in experimental design.
problem Designing and analyzing combinatorial and rounding algorithms for experimental design problems.
method Local search framework for combinatorial algorithms and regret minimization framework for rounding algorithms.
result Unified approach to match and improve all known results in D/A/E-design and obtain new results in unknown settings.
Study of congestion in negative curvature manifolds using fair-division algorithms.
problem Estimating and predicting the size and location of congestion core in negative curvature manifolds.
method Introducing a novel fair-division algorithm to estimate congestion core.
result Demonstrated the effectiveness of fair-division algorithms in estimating congestion core.
DP-NCB algorithm ensures privacy and fairness in bandit decisions.
problem Achieving both privacy and fairness in bandit algorithms.
method Differentially Private Nash Confidence Bound (DP-NCB) framework.
result DP-NCB achieves optimal Nash regret while maintaining privacy.
New methods detect unfairness in multiclass classifiers using DCP.
problem Detecting unfairness in multiclass classifiers.
method Generalizes DCP to multiclass, provides optimization methods.
result Detects classifiers treating a significant fraction of the population unfairly.
In the paper, the martingales and super-martingales relative to a convex set of equivalent measures are systematically studied. The notion of local regular super-martingale relative to a convex set of equivalent measures is introduced and the necessary and sufficient conditions of the local regularity of it in the disc…
This work improves fairness in federated learning by using zero-shot data augmentation.
problem Statistical heterogeneity leads to biased and less uniform accuracy across clients in federated learning.
method Proposes a federated learning system with zero-shot data augmentation to mitigate statistical heterogeneity and improve fairness.
result Empirical results show improved test accuracy and fairness across clients.
We relax demographic parity in regression by enforcing parity at quantile levels and score thresholds.
problem Enforcing full distributional fairness in regression can lead to substantial accuracy loss.
method Introduce (, Z)-fair predictor, derive closed-form solutions, and develop post-processing algorithm.
result The risk gap to the continuous optimum vanishes as the grid is refined, and we enable targeted fairness corrections.
This paper deals with the exact calibration of semidiscretized stochastic local volatility (SLV) models to their underlying semidiscretized local volatility (LV) models. Under an SLV model, it is common to approximate the fair value of European-style options by semidiscretizing the backward Kolmogorov equation using fi…
Improves global counterfactual explanations for model recourse.
problem Inability to provide explanations beyond local instances.
method Investigates and improves Actionable Recourse Summaries (AReS) for global counterfactual explanations.
result Develops more efficient and interactive explainability tools.
Improves multi-objective learning by adapting to local subintervals.
problem Learning a predictor satisfying multiple objectives in an online, changing data setting.
method Adapting an existing multi-objective learning method with an adaptive online algorithm.
result Improves predictions over subgroups and remains robust under distribution shift.
AAggFF improves federated learning fairness through sequential decision making.
problem Achieving client-level fairness in federated learning systems.
method Unified online convex optimization framework for adaptive aggregation strategies.
result AAggFF achieves better client-level fairness in federated learning.
Two simple methods learn fair metrics from data to improve fairness in ML tasks.
problem Lack of widely accepted fair metrics for many ML tasks hinders individual fairness adoption.
method Presented two simple ways to learn fair metrics from various data types.
result Fair training with learned metrics improves fairness on three ML tasks.
New concept of within-group fairness improves AI fairness without sacrificing accuracy.
problem Fairness issues in AI models treating individuals in the same sensitive group unfairly.
method Introducing within-group fairness, proposing mathematical definitions, and developing learning algorithms.
result Improves within-group fairness without sacrificing accuracy and between-group fairness.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
problem Fairness issues in decision-making systems.
method Conditional fairness metric, Derivable Conditional Fairness Regularizer (DCFR), adversarial representation.
result Traditional fairness notations are special cases of the new conditional fairness notation.
Fair Mixup improves fairness in classifiers by interpolating between groups.
problem Ensuring fairness in classifiers during training and evaluation.
method Fair Mixup uses interpolation of samples between groups to enforce fairness constraints.
result Fair Mixup ensures better generalization of fairness in various benchmarks.
Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.
problem Counterfactual fairness is not a necessary condition for algorithmic fairness.
method Analyzed hypothetical scenario and explicated discrimination to develop causal relevance fairness.
result Causal relevance fairness is a modified constraint that circumvents shortcomings of counterfactual fairness.
Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-makingstat.ML
DFL framework improves action and outcome fairness in policy learning.
problem Fairness in policy learning, especially action and outcome fairness.
method Integrates action and outcome fairness into a multi-objective optimization problem using a lexicographic weighted Tchebyshev method.
result DFL framework improves both action and outcome fairness with minimal value reduction.